YINS Distinguished Lecturer Series - Jure Leskovec, Stanford Univ. and Pinterest

Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—–at the level of small network subgraphs—–remains largely unknown. We develop a generalized framework for clustering networks based on higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in neuronal networks, identifies ecological layers in food webs, and automatically detects hubs in transportation networks. Our results imply that networks exhibit rich organizational structures that are exposed by clustering higher-order connectivity patterns.